PISCES: A pipeline for the Systematic, Protein Activity-based Analysis of Single Cell RNA Sequencing Data

Obradovic, Aleksandar; Vlahos, Lukas; Laise, Pasquale; Worley, Jeremy; Tan, Xiangtian; Wang, Alec; Califano, Andrea

While single-cell RNA sequencing provides a new window on physiologic and pathologic tissue biology and heterogeneity, it suffers from low signal-to-noise ratio and a high dropout rate at the individual gene level, thus challenging quantitative analyses. To address this problem, we introduce PISCES (Protein-activity Inference for Single Cell Studies), an integrated analytical framework for the protein activity-based analysis of single cell subpopulations. PISCES leverages the assembly of lineage-specific gene regulatory networks, to accurately measure activity of each protein based on the expression its transcriptional targets (regulon), using the ARACNe and metaVIPER algorithms, respectively. It implements novel analytical and visualization functions, including activity-based cluster analysis, identification of cell state repertoires, and elucidation of master regulators of cell state and cell state transitions, with full interoperability with Seurat’s single-cell data format. Accuracy and reproducibility assessment, via technical and biological validation assays and by assessing concordance with antibody and CITE-Seq-based measurements, show dramatic improvement in the ability to identify rare subpopulations and to assess activity of key lineage markers, compared to gene expression analysis.